Session 1: Introduction to R

Published

7 May 2025

This session will cover:

  • Setting up R and RStudio
  • Basic R syntax and operations
  • Understanding R data types and structures
  • Writing a simple script
  • Functions
  • Installing packages
  • Importing data: CSV

R and RStudio

R and RStudio, what is the difference? Why do I need the two?

R is both a language and program. However working GUI that comes with R is not the most pleasant of experiences.

R can be imagined as the skeleton of a car, the engine, the chassis etc.

RStudio is an integrated development environment (IDE) for R. It is a user friendly interface for the R programming language making writing and running code much easier and more intuitive, as well as providing a host of addins to make your working life easier including: a console, syntax-highlighting editor that supports direct code execution (basically, colours your code and allows a user to select code to run on the fly), tools for plotting, history, debugging and workspace management.

RStudio can be imagined as all the extra parts that make a car comfortable and enjoyable to dive in.

You do not need RStudio to code in R, but it makes life a lot easier.

Other text editors and IDEs like VS Code are also available.

RStudio Layout

RStudio layout with four panels showing in the default positions: the source panel in the top left, the console panel on bottom left, the environment panel in the top right and output panel on the bottom right.

The RStudio layout. Source: Posit
  • Source: This is the text editor which allows you to write scripts.
  • Console: The R console allows for code to be executed interactively and in a piecewise manner.
  • Environments: This panel contains a number of tabs, including the Environment tab which will display saved R objects in the Global Environment.
  • Output: This panel’s tabs allows to view files, plots, packages installed and access help documentation.

RStudio Settings

RStudio settings are accessed through Global Options… from Tools in the menu bar.

There various options that allow you to customise RStudio, from appearance, session setting etc.

RStudio Projects

RStudio Projects are a way to organise your work in R. They help you manage your working directories, workspace and source documents efficiently. It allows you to work on different analytical task in a separate and isolated environment from other work.

To create a RStudio Project, select the Project dropdown in the upper right hand corner.

Select New Project…

Select New Directory

Select New Project

The Basics

R console can be used as a calculator.

10 + 4
[1] 14
10 / 4
[1] 2.5
10 * 4
[1] 40

But that is not very interesting and won’t get us very far.

You can also assign values to variables using the assignment operator <-.

x <- 10
y <- 4

x + y
[1] 14

Writing a Script

Whist it easy to execute code in the console, after a while it comes against limitations. So that is where scripts come in.

To set up a new blank script, click on the little plus sign in the top left of your screen underneath File. Then select R Script. (This can be achieved alternatively by entering SHIFT + CTRL + N)

This will open a new R script in the source panel of RStudio.

It’s good practice to put a name, a date and author at the top of scripts.

# Intro to R - week 1
# Date: the current date
# Author: name

Data Types

At the fundamental level, R has six basic data types. These are:

  • integer
  • double
  • logical
  • character
  • complex (Not considered here)
  • raw (Not considered here)

Numeric: Integer and Double

Numeric values in R can be either integers (whole numbers) and doubles (floating-point numbers), but in general there is not much distinction between both in R and they are interchangeable.

Scientific notation can be used to declare a numeric value. 1e3 is the equivalent of 1000.

Note

In some code you might see a number followed by an L e.g. x <- 2L This explicitly defines x as an integer.

Arithmetic operators

We have already seen some arithmetic operators, but the following operators that be used on numeric types.

Operator Name Example
+ Addition x + y
- Subtraction x - y
* Multiplication x * y
/ Division x / y
^ Exponent x ^ y
%% Modulus x %% y
%/% Integer (Floor) Division x %/% y

Logical

Logical values in R are used to represent boolean data. They have three possible values: TRUE, FALSE and NA.

logical_value <- TRUE
logical_value
[1] TRUE
Note

T and F can also be used as shorthand for TRUE and FALSE, but it is not advisable to use as these shorthands can be overwritten.

Logical operators

Logical operators are used to perform logical operations on boolean values. Here are some common logical operators in R:

Operator Name Example
& AND x & y
| OR x | y
! NOT !x

Comparison operators

Comparison operators are used to compare values, where a boolean value returned.

Operator Name Example
== Equal x == y
!= Not Equal x != y
> Greater Than x > y
< Less Than x < y
>= Greater Than or Equal x >= y
<= Less Than or Equal x <= y
Tip

When trying to determine if a value is NULL or NA, the above comparison operators do not work. i.e. x == NULL or x != NA will not work.

Must use is.null() or is.na() instead.

The logical and comparison operators allow for controlling the flow of how code that gets executed. The most common control flows are if and if-else statements.

# Example of an if-else statement
x <- 10

if (x < 5) {
  print("x is less than 5")
} else if (x < 10) {
  print("x is less than 10")
} else {
  print("x is greater than 10")
}
[1] "x is greater than 10"

Character

Characters in R are used to represent text data. They are surrounded by either a single ' or double " quotes.

char1 <- "hello"
char1
[1] "hello"
# Numbers surrounded by quotes become character type
char2 <- "200"
char2
[1] "200"

Will see more about characters and strings in week 5.

Data Structures

Vectors

Vectors are the simplest type of data structure in R. They can hold numeric, character, or logical data. All the elements of a vector must be of the same type.

The combine function c() is used to create a vector of two or more elements.

# Numeric vector
x <- c(1, 2, 3.5)
x
[1] 1.0 2.0 3.5
# Logical vector
y <- c(TRUE, FALSE, TRUE)
y
[1]  TRUE FALSE  TRUE
# Character vector
defra_group <- c("Defra Core", "EA", "NE", "MMO")
defra_group
[1] "Defra Core" "EA"         "NE"         "MMO"

Factors

Factors are a special type of vector used to handle and store categorical data. A factor can be ordered or unordered.

reviews <- c("good", "bad", "v bad", "bad", "good", "v good")
reviews_fct <- factor(reviews)
reviews_fct
[1] good   bad    v bad  bad    good   v good
Levels: bad good v bad v good

However, the levels are not in a logical order. We can specify the this order using the levels parameter.

reviews_fct_ordered <- factor(reviews, levels = c("v bad", "bad", "good", "v good"))
reviews_fct_ordered
[1] good   bad    v bad  bad    good   v good
Levels: v bad bad good v good

We will come back to factors when plotting (week 4 and 5)

Lists

Lists are a collection of data, that can be of different types.

england <- list(
  capital = "London",
  population = 57112500,
  devolved = FALSE
)

Lists can be listed placed within another list.

scotland <- list(
  captial = "Edinburgh",
  population = 5447000,
  devolved = TRUE
)

wales <- list(
  capital = "Cardiff",
  population = 3132700,
  devolved = TRUE
)

northern_ireland <- list(
  capital = "Belfast",
  population = 1910500,
  devolved = TRUE
)

uk <- list(
  England = england, 
  Scotland = scotland, 
  Wales = wales, 
  "Northern Ireland" = northern_ireland
  )

str(uk)
List of 4
 $ England         :List of 3
  ..$ capital   : chr "London"
  ..$ population: num 57112500
  ..$ devolved  : logi FALSE
 $ Scotland        :List of 3
  ..$ captial   : chr "Edinburgh"
  ..$ population: num 5447000
  ..$ devolved  : logi TRUE
 $ Wales           :List of 3
  ..$ capital   : chr "Cardiff"
  ..$ population: num 3132700
  ..$ devolved  : logi TRUE
 $ Northern Ireland:List of 3
  ..$ capital   : chr "Belfast"
  ..$ population: num 1910500
  ..$ devolved  : logi TRUE

Matrix and Arrays

Matrices are two-dimensional and arrays are multi-dimensional, homogeneous data structures. All elements in a matrix or array must be of the same type. Will not be used much in this series.

# Example of a matix
X <- matrix(1:10, nrow = 2)
X
     [,1] [,2] [,3] [,4] [,5]
[1,]    1    3    5    7    9
[2,]    2    4    6    8   10
# Example of an array
Y <- array(1:12, dim = c(2, 2, 3))
Y
, , 1

     [,1] [,2]
[1,]    1    3
[2,]    2    4

, , 2

     [,1] [,2]
[1,]    5    7
[2,]    6    8

, , 3

     [,1] [,2]
[1,]    9   11
[2,]   10   12

Dataframes

Data frames are one of the most commonly used data structures in R for storing tabular data. Similar to matrices above, they consist of columns and rows of data. Unlike matrices, data different across columns. Data must be of the same type within column though.

Converting the UK data above

countries <- c("England", "Scotland", "Wales", "Northern Ireland")
capital <- c("London", "Edinburgh", "Cardiff", "Belfast")
population <- c(57112500, 5447000, 3132700, 1910500)
devolved <- c(FALSE, TRUE, TRUE, TRUE)

uk_df <- data.frame(countries, capital, population, devolved)
uk_df
         countries   capital population devolved
1          England    London   57112500    FALSE
2         Scotland Edinburgh    5447000     TRUE
3            Wales   Cardiff    3132700     TRUE
4 Northern Ireland   Belfast    1910500     TRUE

Functions

Functions in R are a fundamental part of programming in the language. They allow you to encapsulate code into reusable blocks, making your scripts more modular and easier to maintain.

discount_factor <- function(year, r = 0.035) {
  dis_fct <- 1 / (1 + r)^year
  return(dis_fct)
}

Packages

Packages in R are collections of functions, data, and compiled code that extend the capabilities of R. They are generally created for specialised tasks.

Packages are available to download from a number of sources, with CRAN being the main repository for packages. Currently there are 22,416 packages available on CRAN.

The install.packages() command is used to install a package from CRAN.

# Install tidyverse
install.packages("tidyverse")

Once installed, a package needs to be loaded to be able to use the functions and data of the package. This done by using the library() command.

The tidyverse

The tidyverse is a collection of R packages that are designed to make data analysis in R easier and more consistent. It includes packages for data manipulation, visualization, and more..

The tidyverse made up of nine core packages which are loaded when library(tidyverse) is called, with each package specialising in an area of data analysis/data science.

  • readr for reading rectangular data (CSV, TSV, txt files).
  • dplyr provides a grammar of data manipulation through set of “verbs”.
  • tidyr to create tidy data.
  • ggplot2 used for plotting data using the grammar of graphics.
  • purrr
  • stringr
  • lubridate
  • forcats used to deal with factor objects.
  • tibble

The advantage of tidyverse packages is that they share a design philosophy, common grammar and data structures.

Importing Data - CSV

CSV (Comma-separated values) files are widely used for data storage and exchange due to their simplicity and compatibility with various software applications. Therefore importing data from CSV is important skill.

Within R, there is the read.csv() function. However, we will look at the read_csv() function from the {readr} package installed with the tidyverse, as it provides a number of advantages.

library(readr)

cereals_df <- read_csv("data/cereals.csv")
Rows: 139 Columns: 8
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (8): Year, Wheat, Barley, Oats, Rye, Mixed Corn, Triticale, Oilseed Rape

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
tail(cereals_df)
# A tibble: 6 × 8
   Year Wheat Barley  Oats   Rye `Mixed Corn` Triticale `Oilseed Rape`
  <dbl> <dbl>  <dbl> <dbl> <dbl>        <dbl>     <dbl>          <dbl>
1  2018   7.8    5.7   5      NA           NA        NA            3.4
2  2019   8.9    6.9   5.9    NA           NA        NA            3.3
3  2020   7      5.9   4.9    NA           NA        NA            2.7
4  2021   7.8    6.1   5.6    NA           NA        NA            3.2
5  2022   8.6    6.6   5.7    NA           NA        NA            3.7
6  2023   8.1    6.1   5      NA           NA        NA            3.1

What if my data is across a number of CSVs? read_csv() allows data to be read from multiple files once it is in the same format across the different CSV files.

files <- list.files("data/wine-imports/", full.names = TRUE)

wine_imports_df <- read_csv(files)
Rows: 74278 Columns: 11
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): cod, port, coo
dbl (8): perref, type, comcode, sitc, mode, value, mass, supp_unit

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(wine_imports_df)
# A tibble: 6 × 11
  perref  type  comcode  sitc cod   port  coo    mode    value   mass supp_unit
   <dbl> <dbl>    <dbl> <dbl> <chr> <chr> <chr> <dbl>    <dbl>  <dbl>     <dbl>
1 202301     1 22041011 11215 FR    <NA>  FR        0 22139627 767412    766616
2 202301     1 22041011 11215 FR    ZZZ   <NA>     NA  1544890  25788     25894
3 202301     1 22041011 11215 FR    LON   FR       10  2591004 122650    122648
4 202301     1 22041011 11215 FR    MED   FR       60    93855   6615      6615
5 202301     1 22041011 11215 FR    DOV   FR       60   936092  47963     47994
6 202301     1 22041011 11215 FR    LGP   FR       10     4068    179        47